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Ulrich Strötz [11-2014]:

Calculating timber harvest costs based solely on spatial predictors exemplified by the Colorado State Forest

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Optimization models for ecological forestry approaches require consideration of a variety of spatial features, including Harvest Costs, in order to maximize triple bottom line returns. The models require a pre-generated dataset with the potential Harvest Costs for the entire landscape in order to iterate through millions of potential solutions and compare results in terms of an objective function. Since the composition and the structure of the forest systems are usually not available for an entire landscape, a model is required that calculates Harvest Costs solely based on Spatial Predictors. Spatial Predictors can be determined via Geographic Information Systems. Currently no existing study investigates the significance of Spatial Predictors on Timber Harvest Cost. Therefore it is also not known if the significance of Spatial Predictors on Harvest Cost is high enough to calculate Timber Harvest Costs solely based on Spatial Predictors. This study answers these research questions with the following method: A dataset containing 160,000 test units based on existing harvest data of the Colorado Sate Forest is created. The dataset contains for each unit the Spatial and Non-Spatial Predictors of Timber Harvest Costs. Each unit is run through a created Harvest Cost Model, which is based on existing literature and equations. The Harvest Cost Model returns for each unit a Harvest Cost per ton. The spatial and non-spatial input data are then used as independent variables in a multiple linear regression model, with the resulting Harvest Cost from the model as the dependent variable. From the created regression model, a spatially explicit regression model is derived by excluding the non-spatial variables. The spatially explicit regression model calculates Harvest Costs solely based on Spatial Predictors. Finally, based on the spatially explicit regression model, a Cost Surface is created. The Cost Surface contains the Harvest Cost for any given location throughout the landscape. The created spatially explicit regression model has an R-squared of 0.42. Therefore Spatial Predictors predict 42% of Timber Harvest Costs. Calculating Timber Harvest Costs with an accuracy of 42% is not enough to calculate absolute Harvest Costs solely based on Spatial Predictors. But for optimization models relative Harvest Costs are sufficient, since relative Harvest Cost allows the comparison of Costs of different stands and scenarios. An accuracy of 42% is then enough to estimate relative Harvest Costs. Therefore the results of this research make it possible to include Harvest Costs in optimization models for ecological forestry approaches. With their inclusion optimization models are significantly improved.


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